Update tools/normal_distribution.py

tools/normal_distribution.py

@@ -36,7 +36,7 @@ def generate_normal_distribution(mean: float, std_dev: float, count: int = 10000
     return samples
 
 @tool
-def create_histogram_and_theorical_pdf(mean: float, std_dev:float, random_numbers:list)->matplotlib.figure.Figure:
+def create_histogram_and_theorical_pdf(mean: float, std_dev:float, random_numbers:list)->str:
     """Generate a histogram of random numbers and overlay the theoretical 
     probability density function (PDF) of a normal distribution. 
     Return the histogram as a base64-encoded string.
@@ -46,6 +46,9 @@ def create_histogram_and_theorical_pdf(mean: float, std_dev:float, random_number
         std_dev: The standard deviation of the normal distribution.
         random_numbers: A list of random numbers generated from a 
                                          normal distribution.
+
+    Returns:
+        str: The graphics for the histogram and probability density function (PDF) on string format
     """
     # Prepare data for plotting
     hist_data = [0] * 50  # Create a list to hold histogram data
@@ -72,21 +75,16 @@ def create_histogram_and_theorical_pdf(mean: float, std_dev:float, random_number
             for x in x_values
     ]
 
-    # Create a figure
-    fig = plt.figure(figsize=(10, 6))
-    ax = fig.add_subplot(111)  # Create an Axes object on the figure
-
-    # Add histogram as a bar plot to the Axes
-    ax.bar(x_values, hist_data, width=bin_width, alpha=0.6, color='g', label='Histogram')
-
-    # Add theoretical PDF as a line plot to the same Axes
-    ax.plot(x_values, pdf_values, 'k', linewidth=2, label='Theoretical PDF')
-
-    # Set title and labels
-    ax.set_title(f'Histogram of {len(random_numbers)} Random Numbers\nMean: {mean:.2f}, Std Dev: {std_dev:.2f}')
-    ax.set_xlabel('Value')
-    ax.set_ylabel('Density')
-    ax.legend()
-    ax.grid()
-
-    return fig
+     # Scale for ASCII output
+    max_hist = max(hist_data) if hist_data else 1  # Avoid division by zero
+    max_pdf = max(pdf_values) if pdf_values else 1  # Avoid division by zero
+    max_height = 20  # Maximum height of the ASCII histogram
+
+    # Building the ASCII graph as a string
+    ascii_graph = "Histogram (|: Counts, -: PDF)\n"
+    for i in range(len(hist_data)):
+        hist_count = int((hist_data[i] / max_hist) * max_height)
+        pdf_count = int((pdf_values[i] / max_pdf) * max_height)
+        ascii_graph += f"{'|' * hist_count} {'-' * pdf_count} {x_values[i]:.2f}\n"
+    
+    return ascii_graph